Tobacco leaf full life cycle quality reliable traceability system and method

Abstract

The application discloses a tobacco full-life-cycle quality credible traceability system and method, and relates to the technical field of Internet of Things and agricultural informatization. The system comprises a plurality of Internet of Things data anchoring terminals, a multi-chain collaborative blockchain network and a credible query service platform. The Internet of Things data anchoring terminals are used for collecting business data and generating source data packets with credible time-space stamps and device identity certificates. The multi-chain collaborative blockchain network is composed of a production alliance sub-chain and a supervision main chain, and is used for storing full-amount production data and key supervision summaries, respectively. The credible query service platform is used for returning traceability information or verification proofs according to permissions. The application can realize the balance of source tamper resistance, privacy protection and supervision transparency of tobacco full-life-cycle data, and provide automatic and logically rigorous traceability services.

Description

Technical Field

[0001] This invention relates to the fields of Internet of Things and agricultural information technology, and in particular to a reliable traceability system and method for the entire life cycle quality of tobacco leaves. Background Technology

[0002] As a vital economic crop sector, the tobacco industry's product quality and safety directly impact consumer health and the industry's sustainable development. Currently, the industry has established some preliminary quality traceability systems based on centralized databases. These systems primarily use QR codes on products to store information from various stages of planting, harvesting, drying, and processing—whether manually entered or collected by sensors—in a central server for later retrieval. Such systems typically employ a hierarchical data management architecture, relying on a single management organization for data maintenance and updates. They utilize digital signature technology to verify data integrity during transmission and leverage cloud computing platforms for centralized storage and rapid retrieval of massive traceability data. With the development of IoT technology, some existing solutions are beginning to incorporate BeiDou positioning modules to record operational locations or utilize consortium blockchain technology to store hash values ​​of key nodes, attempting to enhance data tamper-proof capabilities through distributed ledger characteristics, thus forming preliminary digital archives from the field to the factory.

[0003] However, in the face of the long tobacco production chain, many participants, and complex environment, existing technologies still have problems such as the inability to fundamentally guarantee the authenticity of data sources from the physical source, the difficulty in balancing the detailed data of full production with regulatory compliance requirements, and the inability of discrete agricultural events to automatically form a tight logical closed loop. These problems limit the credibility and penetration of traceability information. Summary of the Invention

[0004] Purpose of the invention: To provide a reliable traceability system and method for the entire life cycle quality of tobacco leaves, so as to at least solve one of the problems existing in the prior art.

[0005] Technical solution: An adaptive control system for tobacco curing process based on dynamic response feedback, comprising: Multiple IoT data anchoring terminals are deployed at the operating equipment or key nodes in various stages of tobacco production. Each IoT data anchoring terminal is used to collect business data when the corresponding key event is triggered, and generate a source data packet with a trusted spatiotemporal stamp and a unique device identity certificate. A multi-chain collaborative blockchain network is connected to the IoT data anchoring terminal and consists of at least one production alliance sub-chain and at least one regulatory main chain. The production alliance sub-chain is used to perform consensus verification on the source data packet and then distribute the evidence. The regulatory main chain is used to receive and store the aggregated key regulatory summary data from the production alliance sub-chain through a cross-chain protocol. The trusted query service platform communicates with the multi-chain collaborative blockchain network and is also connected to the production alliance sub-chain and the regulatory main chain. It is used to receive query requests and, according to the permission level of the query request, retrieve and return traceability information or verification proof based on the blockchain-stored evidence data.

[0006] Preferably, the IoT data anchoring terminal integrates a Beidou / GNSS positioning module, a security chip storing a unique device digital fingerprint, and a communication module; The generation of the source data packet with a trusted spatiotemporal stamp and a unique device identity credential includes: In response to a trigger signal generated when the operating equipment performs a critical operation, the corresponding service data is received; The BeiDou / GNSS positioning module is invoked to obtain the current UTC time and geographic coordinates, and a BeiDou time-space stamp is generated. Read the unique device digital fingerprint from the security chip; At least the business data, the BeiDou time stamp, and the device digital fingerprint are hashed to generate a digital digest; The business data, BeiDou time stamp, device digital fingerprint, and digital digest are encapsulated into the source data packet and broadcast to the multi-chain collaborative blockchain network through the communication module.

[0007] Preferably, the operating mechanism of the multi-chain collaborative blockchain network includes: The production alliance subchain consists of multiple entity nodes in the tobacco industry chain and adopts a practical Byzantine fault-tolerant consensus algorithm to store data in the production process. Data association smart contracts and regulatory summary generation smart contracts are deployed on the production alliance subchain. The regulatory main chain consists of at least one industry regulatory agency node, which is used to store key regulatory summary data and violation evidence hash values ​​generated by the regulatory summary generation smart contract and synchronized through the cross-chain protocol.

[0008] Preferably, the multi-chain collaborative blockchain network also includes a cross-chain relay component; The cross-chain protocol is configured as follows: the cross-chain relay component receives a cross-chain request submitted by the regulatory summary generation smart contract, which contains the key regulatory summary data and the original transaction Merkel proof; after verifying the validity of the Merkel proof, the key regulatory summary data is packaged into a new block of the regulatory main chain, and a regulatory evidence hash is generated. The main regulatory chain can verify the authenticity of the evidence data stored on the production alliance sub-chain based on the regulatory evidence hash and the Merkel proof, but it does not have the right to directly access the full amount of detailed data stored on the production alliance sub-chain.

[0009] Preferably, the data-associated smart contract is configured to execute the following logic: Continuously monitor and receive multiple source data packets that are stored on the production alliance subchain and carry the same geographic unit identifier or the same batch code; According to the time order of the BeiDou time and space stamps in each source data packet, the source data packets representing discrete agricultural events are automatically linked together into an event chain with a temporal logical relationship. In response to receiving a source data packet from the harvesting stage that carries a specific batch code, the system automatically digitally binds the batch code to all concatenated source data packets belonging to the same production unit, generating and storing a complete digital batch file.

[0010] Preferably, before the data-associated smart contract executes the listening and receiving logic, it further includes: Parse the geographic coordinates contained in the BeiDou time and space stamp in each source data packet, determine whether the geographic coordinates fall within one or more predefined geofences, and associate the source data packets that are determined to fall within the same geofence with the same geographic unit identifier.

[0011] Preferably, in the source data packet generated by the IoT data anchoring terminal, the BeiDou spatiotemporal stamp and the device digital fingerprint together constitute two-factor anchoring information, which is used to provide dual proof of spatiotemporal non-repudiation and device identity non-forgeability for the business data. The device digital fingerprint is generated by combining a session random number generated by the true random number generator of the security chip with the device ID to ensure that each generated fingerprint is unique.

[0012] To achieve the above objectives, according to another aspect of this application, a reliable traceability method for the quality of tobacco leaves throughout their entire life cycle is provided.

[0013] A method for reliable traceability of tobacco leaf quality throughout its entire lifecycle, according to the system described in this application, includes the following steps: Data source anchoring: When key events occur in each tobacco production process, IoT data anchoring terminals collect business data in real time and attach BeiDou spatiotemporal stamps and device digital fingerprints to the business data to generate trusted source data packets. Subchain consensus notarization involves broadcasting the trusted source data packet to the production alliance subchain network. After consensus verification by multiple nodes, the packet is packaged and uploaded to the chain to form an immutable notarization record. Smart contract aggregation is achieved by data-associating smart contracts deployed on the production alliance subchain, which continuously and dynamically aggregate the evidence records corresponding to multiple discrete events belonging to the same production unit or the same batch into logically associated batch files. Cross-chain regulatory synchronization involves a regulatory summary generation smart contract extracting key regulatory summary information from the batch of files and synchronizing this key regulatory summary information to the regulatory main chain for evidence storage via a cross-chain protocol. Multi-role trusted query: The trusted query service platform receives and responds to query requests from different roles, and returns traceability information or encrypted proof of data integrity backed by blockchain according to the query permissions.

[0014] Preferably, the batch files dynamically aggregated into logical associations include: The data association smart contract parses the BeiDou time and space stamp in each trusted source data packet and extracts its geographical coordinates; Call the pre-configured geofence database to determine the parcel identifier to which the geographic coordinates belong; Trusted source data packets with the same plot identifier but different operation types are sorted and linked according to the time sequence of their BeiDou spatiotemporal stamps to form a time-series agricultural operation chain for that plot. When a trusted source data packet carrying a batch code is received during the harvesting process, the entire time-series agricultural operation chain is hashed and associated with the batch code to generate a digital batch file.

[0015] Preferably, the step of returning blockchain-backed traceability information or encrypted proof of data integrity based on query permissions includes: In response to a lightweight query request from a consumer terminal, the trusted query service platform retrieves and returns a key information graph corresponding to the query identifier from the regulatory main chain or the production alliance sub-chain. The key information graph includes at least a visual summary of the place of origin, harvest time, and major agricultural records. In response to a deep verification request from the regulator's terminal, the trusted query service platform calls the cross-chain verification interface to obtain the regulatory evidence hash and cross-chain verification proof corresponding to the target batch from the main regulatory chain, and to obtain the transaction hash and Merkel proof corresponding to the specific agricultural event from the production alliance sub-chain. The platform then generates and returns a comprehensive verification report containing multi-layered encrypted verification results, which can be verified by an independent blockchain explorer.

[0016] Beneficial Effects: This application provides a reliable traceability system for the entire life cycle of tobacco leaf quality. This solution deploys IoT data anchoring terminals at each stage of tobacco production, collects business data when key events are triggered, and generates source data packets with trusted spatiotemporal stamps and unique device identity credentials. The source data packets are distributed and stored after consensus verification on the production alliance sub-chain using a multi-chain collaborative blockchain network. The aggregated key regulatory summary data is stored on the regulatory main chain through a cross-chain protocol. Finally, the trusted query service platform retrieves and returns traceability information or verification certificates from the corresponding blockchain according to the permission level of the query request. By integrating BeiDou / GNSS positioning modules and security chips into IoT data anchoring terminals, each piece of on-chain data is bound to unforgeable spatiotemporal coordinates and device digital fingerprints. This establishes a two-factor anchoring mechanism of spatiotemporal + identity at the source of data generation, effectively solving the problem of easily tampered or forged source data in traditional traceability systems. Based on the layered architecture of the production alliance sub-chain and the regulatory main chain, all detailed production data is kept in the industry's internal sub-chain, while only key regulatory summaries are synchronized to the high-authority main chain. This protects enterprise data sovereignty and commercial privacy, while meeting the regulatory agencies' need for penetrating monitoring of key risks and avoiding the privacy leakage risk caused by full data disclosure. By using data-linked smart contracts to automatically listen to and sequentially connect discrete agricultural events and trigger batch binding logic at the harvesting stage, logically rigorous digital batch archives can be dynamically generated, overcoming the defects of broken traceability chains or logical confusion caused by manual association. This effectively solves core technical problems such as the difficulty in ensuring data authenticity, balancing privacy and regulation, and automating traceability logic throughout the entire life cycle of tobacco leaves. This solution constructs a complete and trustworthy closed loop from physical world events to on-chain digital evidence storage, significantly improving the judicial evidentiary value and industry governance efficiency of traceability data, and providing highly reliable technical support for the digital transformation of the tobacco industry.

[0017] In summary, this application has formed a logically consistent, secure and controllable traceability system through deep integration of software and hardware and multi-chain collaboration mechanism. It not only ensures the immutability and spatiotemporal verifiability of data, but also realizes trust-minimized collaboration with multiple parties, which has extremely high technical application value and promotion prospects. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall architecture of the reliable traceability system for the entire life cycle quality of tobacco leaves provided in this application; Figure 2a The diagram shows the hardware module configuration of the IoT data anchoring terminal in the data packaging flowchart of the reliable traceability system for the entire life cycle quality of tobacco leaves provided in this application. Figure 2bThe internal module structure and data packaging flowchart of the IoT data anchoring terminal in the tobacco leaf full life cycle quality trust traceability system provided in this application are shown in the time sequence flowchart of the data packaging process. Figure 3a The diagram shows the network topology of the multi-chain collaborative blockchain network in the data synchronization and cross-chain verification mechanism of the tobacco leaf full life cycle quality trusted traceability system provided in this application. Figure 3b The time sequence diagram of cross-chain data synchronization in the multi-chain collaborative blockchain network of the trusted traceability system for the whole life cycle quality of tobacco leaves provided in this application; Figure 4 The query response sequence diagram from consumer scanning to on-chain data verification in the tobacco leaf full life cycle quality trusted traceability system provided in this application; and Figure 5 The flowchart of the reliable traceability method for the entire life cycle quality of tobacco leaves provided in this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] To better understand the background technology of this application, the following explanation is provided: In the full life-cycle management of the tobacco industry, from planting, field management, and harvesting to subsequent curing, re-curing, and processing, numerous links and stakeholders are involved. Existing tobacco quality traceability systems largely rely on centralized databases to store information manually entered or collected by equipment at each stage, and then display this information to consumers or regulators via QR codes. However, this traditional approach has significant technical flaws: First, due to the lack of effective anti-tampering mechanisms at the data source, manually entered or data collected by ordinary sensors is easily modified or forged, making it difficult to eradicate source-level fraud and casting doubt on the authenticity and non-repudiation of the data; second, each production stage often forms information silos, with data scattered across the private systems of different entities, lacking a unified and reliable sharing mechanism, leading to broken traceability chains or logical inconsistencies; third, a single centralized storage architecture faces the risk of single point of failure; if the server is damaged or attacked, massive amounts of traceability data may be lost or maliciously tampered with; finally, existing technologies are insufficient in balancing commercial privacy and government regulatory needs, either compromising corporate trade secrets due to excessive disclosure or making it impossible for regulators to penetrate and verify information due to closed management. To address the aforementioned issues, this application proposes a framework based on digital anchoring of physical world events and multi-chain collaborative governance, aiming to build a trusted digital archive system that spans the entire life cycle of tobacco leaves.

[0022] Based on the above issues, please refer to Figure 1 This application provides a reliable traceability system for the entire lifecycle quality of tobacco leaves. The system includes: multiple IoT data anchoring terminals, deployed at operational equipment or key nodes in various stages of tobacco production. Each IoT data anchoring terminal is used to collect business data when a corresponding key event is triggered, and generate a source data packet with a reliable spatiotemporal stamp and a unique device identity certificate; a multi-chain collaborative blockchain network, communicating with the IoT data anchoring terminals, and consisting of at least one production alliance sub-chain and at least one regulatory main chain. The production alliance sub-chain is used for distributed storage after consensus verification of the source data packet, and the regulatory main chain is used to receive and store key regulatory summary data from the production alliance sub-chain after aggregation processing through a cross-chain protocol; and a reliable query service platform, communicating with the multi-chain collaborative blockchain network and data-connected to both the production alliance sub-chain and the regulatory main chain, for receiving query requests and, according to the permission level of the query request, obtaining and returning traceability information or verification proof based on the blockchain-stored data from the corresponding blockchain.

[0023] Specifically, multiple IoT data anchoring terminals can refer to intelligent data acquisition devices deployed throughout the entire process of tobacco agricultural production and primary processing. In the embodiments of this application, these terminals can be installed on operating equipment such as transplanters, plant protection drones, fertilizer applicators, and harvesting vehicles, or fixedly installed at monitoring locations at key nodes such as curing barns and re-curing production lines. The core function of the IoT data anchoring terminals is to achieve event-driven data acquisition, that is, instead of continuously recording invalid data, the acquisition program is automatically started when the operating equipment performs specific key operations (such as spraying pesticides, picking tobacco leaves, or entering the curing stage). The business data collected by the terminal can refer to data reflecting the specific content of the current agricultural operation, such as the operation type, the name and dosage of agricultural inputs used, the operating area, and environmental parameters. To ensure the authenticity of the data source, the IoT data anchoring terminals must attach a trusted spatiotemporal stamp and a unique device identity certificate when generating source data packets. A trusted spatiotemporal stamp can refer to a combination of high-precision time information and geographic coordinates obtained from the BeiDou Navigation Satellite System (BDS) or the Global Navigation Satellite System (GNSS). Its time accuracy can reach the millisecond level, and its positioning accuracy can reach the centimeter or meter level, thus giving each piece of business data an unforgeable time-location tag. A unique device identity credential can refer to a hardware fingerprint originating from the terminal's internal security chip. This fingerprint is generated based on the chip's built-in true random number generator and the device's unique identifier, making it uncopyable. Through this three-element binding mechanism of business data + spatiotemporal stamp + device fingerprint, IoT data anchoring to the terminal ensures that the on-chain data can truly map physical world operational events. Any subsequent data tampering will result in hash verification failure, thus solving the technical problem of source data forgery.

[0024] A multi-chain collaborative blockchain network can refer to a layered distributed ledger system designed to resolve the contradiction between the limitations of a single blockchain in simultaneously meeting the demands of high-frequency transaction processing, data privacy protection, and stringent regulatory requirements. This network consists of at least one production alliance sub-chain and at least one regulatory main chain, with controlled data interaction between the two through a specific cross-chain protocol. The production alliance sub-chain can be a consortium blockchain network jointly maintained by multiple entity nodes in the tobacco industry chain (such as planting cooperatives, curing plants, and re-drying plants). In this sub-chain, each node uses Practical Byzantine Fault Tolerance (PBFT) or other efficient consensus algorithms to quickly verify and package source data packets from IoT data anchoring terminals. The production alliance sub-chain is primarily used to store comprehensive and detailed production process data, including detailed records of tobacco leaf growth, which constitute the core commercial assets of the enterprise. Therefore, distributed notarization within the sub-chain ensures both data immutability and data sovereignty and privacy within the alliance members. The regulatory main chain can refer to a blockchain network with a higher level of authority, built by industry regulatory agencies as core nodes. The main regulatory chain does not directly store all detailed data from the production process to avoid data redundancy and privacy leaks. Instead, it receives and stores aggregated key regulatory summary data from the production alliance sub-chains via a cross-chain protocol. This key regulatory summary data can refer to hash values ​​or brief descriptions extracted through smart contracts that involve quality and safety red lines, evidence of violations, or key batch flow information. Through this collaborative mechanism of storing details on sub-chains and summaries on the main chain, the multi-chain collaborative blockchain network achieves a regulatory model where data is available but not visible. This satisfies the government's need for penetrating supervision of tobacco quality while effectively protecting the commercial privacy of production enterprises.

[0025] A trusted query service platform refers to an application service system that provides a unified access interface for different user roles, acting as a bridge between users and the underlying multi-chain collaborative blockchain network. This platform establishes data connections with both the production alliance sub-chain and the regulatory main chain, possessing the ability to parse data structures on different chains. When a user initiates a query request, the trusted query service platform first authenticates the requester's identity and identifies their permission level. Permission levels can be divided into different tiers, such as ordinary consumers, production enterprise personnel, and industry regulators, with different levels corresponding to different data access depths. For low-permission query requests (such as consumers scanning product QR codes), the platform primarily retrieves and returns traceability information based on blockchain-stored data from the public indexes of the regulatory main chain or the production alliance sub-chain. This information is typically presented as a summary of key nodes in the form of a visual graph, such as the place of origin, harvest time, and major agricultural records, excluding sensitive commercial details. For high-privilege query requests (such as in-depth verification or dispute arbitration by regulators), the platform has the right to invoke the cross-chain verification interface to obtain regulatory evidence hashes and cross-chain verification proofs from the regulatory main chain, and further request Merkel proofs for specific transactions from the production alliance sub-chain. Finally, it generates and returns a verification proof containing multi-layered encrypted verification results. This differentiated return mechanism based on permission levels ensures the security and compliance of traceability information, enabling all users to obtain trustworthy data matching their identities.

[0026] The core innovation of this application lies in constructing an integrated trusted traceability system encompassing physical sensing, dual-factor anchoring, multi-chain layering, and access control. This system deeply integrates BeiDou high-precision spatiotemporal information with hardware security fingerprints, achieving an irreversible mapping from physical world events to digital world credentials at the source of data generation. Furthermore, by utilizing a layered architecture of production alliance sub-chains and regulatory main chains, it cleverly strikes a balance between efficient data flow, privacy protection, and robust regulation. Finally, through access control of the trusted query service platform, it enables the orderly release of data value.

[0027] The working process and principle of this application are as follows: When the equipment in each stage of tobacco production performs critical operations, the IoT data anchoring terminal deployed on it is triggered, collects business data in real time, and simultaneously calls the Beidou / GNSS module to obtain the current precise time and coordinates to generate a trusted spatiotemporal stamp, while reading the unique device digital fingerprint in the security chip; the terminal performs hash operation on the business data, trusted spatiotemporal stamp, and device digital fingerprint to generate a digital digest, and encapsulates the three into a source data packet and broadcasts it to the multi-chain collaborative blockchain network; after receiving the source data packet, the nodes of the production alliance sub-chain verify its validity through a consensus algorithm, package it onto the chain to form an immutable data packet. The system records all data; subsequently, a regulatory summary generation smart contract deployed on the production alliance subchain automatically runs, extracting key regulatory summary data from the full data. This summary data and corresponding Merkel proofs are then synchronized to the regulatory main chain for storage via a cross-chain relay component, while the original detailed data remains in the subchain. When a user initiates a query through the trusted query service platform, the platform, based on the user's permission level, directly returns on-chain summary information for ordinary queries and compares the Merkel proof from the subchain with the storage hash from the main chain for verification via a cross-chain protocol. Finally, it provides the user with traceability information or integrity proof endorsed by the blockchain.

[0028] Through the above technical solutions, this application achieves the following beneficial effects: By employing an IoT data anchoring terminal integrating BeiDou spatiotemporal stamps and device digital fingerprints, the possibility of data forgery is eliminated from the physical source, establishing a trust foundation where on-chain data represents real-world events. This completely solves the technical problem of easily tampered and non-repudiable source data in traditional traceability systems. Furthermore, by designing a multi-chain collaborative architecture where the production alliance sub-chain stores full details and the regulatory main chain only stores key summaries, and by achieving controlled data synchronization through cross-chain protocols, this approach not only protects the data sovereignty and commercial privacy of production enterprises but also meets the regulatory agencies' need for penetrating supervision of key quality information, achieving a structural balance between privacy protection and regulatory transparency. Finally, by constructing a trusted query service platform based on permission levels, which can dynamically return traceability information or encrypted proofs of corresponding depth according to different user roles, this improves the system's flexibility and security, provides consumers with a convenient trust verification channel, provides regulators with an efficient enforcement tool, and provides producers with technical means for data assetization, comprehensively enhancing the quality control capabilities and brand value of the entire tobacco leaf industry chain.

[0029] Furthermore, the IoT data anchoring terminal integrates a BeiDou / GNSS positioning module, a security chip storing a unique device digital fingerprint, and a communication module; The generation of the source data packet with a trusted spatiotemporal stamp and a unique device identity credential includes: In response to a trigger signal generated when the operating equipment performs a critical operation, the corresponding service data is received; The BeiDou / GNSS positioning module is invoked to obtain the current UTC time and geographic coordinates, and a BeiDou time-space stamp is generated. Read the unique device digital fingerprint from the security chip; At least the business data, the BeiDou time stamp, and the device digital fingerprint are hashed to generate a digital digest; The business data, BeiDou time stamp, device digital fingerprint, and digital digest are encapsulated into the source data packet and broadcast to the multi-chain collaborative blockchain network through the communication module.

[0030] Specifically, the IoT data anchoring terminal refers to a hardware device deployed at various operational equipment or key nodes in tobacco production. Its core function is to collect data in real time and assign it a cryptographically trusted identity when physical events occur. This terminal may include a main control unit 201, a BeiDou / GNSS positioning module 202, a security chip 203, a communication unit 207, a data interface unit 205, a storage unit 206, and a power management unit 208. The main control unit 201 can be an ARM architecture processor or an embedded microcontroller, used to coordinate the work of various modules and execute data packaging logic; the power management unit 208 is used to provide stable voltage input to the above components to adapt to the complex power supply environment in the field. The terminal's functional role in the overall solution is as a root of trust, ensuring the authenticity of the source of the data uploaded to the blockchain. It works in conjunction with a multi-chain collaborative blockchain network to broadcast source data packets with spatiotemporal and identity markers to the network, forming an immutable initial evidence record.

[0031] The BeiDou / GNSS positioning module 202 can refer to a radio frequency receiver component integrating high-precision positioning functionality, which can receive satellite signals through antenna 2021. This module can be used to parse satellite navigation messages, output the precise UTC time and latitude and longitude coordinates of the current moment, and the positioning accuracy can be set according to actual conditions, such as meter-level, decimeter-level, or centimeter-level (if using RTK technology). In this application, the BeiDou / GNSS positioning module 202 cooperates with the main control unit 201. When it receives the trigger signal from the operating equipment, it immediately locks the current spatiotemporal information and generates a BeiDou spatiotemporal stamp. This BeiDou spatiotemporal stamp, as one of the key elements of the source data packet, is used to prove the specific time and geographical location of the business data generation, thereby achieving spatial immutability and temporal immutability at the technical level.

[0032] The security chip 203 can refer to a hardware encryption component that conforms to national cryptographic standards or internationally recognized security levels, and it internally stores a unique device identifier and key system. This security chip 203 can be used to store a unique device digital fingerprint. This fingerprint can be generated based on a session random number generated by the chip's built-in true random number generator and the device ID, ensuring that each generated fingerprint is unique and unpredictable. In this application, the security chip 203 is connected to the main control unit 201 via an SPI bus or other secure interface. When the main control unit 201 initiates a read request, the security chip 203 returns the current device digital fingerprint. This fingerprint, as another key element of the source data packet, is used to prove the identity of the device from which the data originates, ensuring that the device identity cannot be impersonated and preventing unauthorized terminals from forging data to access the network.

[0033] The process of generating a source data packet with a trusted spatiotemporal stamp and a unique device identity credential can refer to a series of data processing actions executed by the main control unit 201 according to a preset logical sequence. First, in response to electrical or digital signal triggering signals generated when the equipment performs key operations (such as applying pesticides, harvesting, or drying), the main control unit 201 receives corresponding business data through the data interface unit 205. This business data may include the operation type, pesticide name, dosage, environmental parameters, etc., and its specific format and content can be set according to the actual application scenario. Second, the main control unit 201 calls the BeiDou / GNSS positioning module 202 to obtain the precise UTC time and geographic coordinates of the current moment and formats it as a BeiDou spatiotemporal stamp. Third, the main control unit 201 reads the unique device digital fingerprint from the security chip 203. Subsequently, the main control unit 201 concatenates the business data, the BeiDou spatiotemporal stamp, and the device digital fingerprint, and calls a hash algorithm (such as SHA-256, SM3, etc.) to generate a fixed-length digital digest. This digital digest is equivalent to a digital fingerprint of the three elements mentioned above; any slight alteration to the original data will result in a significant change in the digest value. Finally, the main control unit 201 encapsulates the business data, BeiDou time stamp, device digital fingerprint, and digital digest into a complete source data packet, which can be temporarily stored in the storage unit 206 and then broadcast to the multi-chain collaborative blockchain network via the communication unit 207.

[0034] The communication module, or communication unit 207, can refer to a wireless transmission component supporting multiple communication protocols, such as a 4G / 5G cellular communication module, a BeiDou short message communication module, or a LoRa / NB-IoT module. This communication module can automatically switch communication modes based on network coverage; for example, it can use 4G / 5G transmission when public network coverage is good, and use BeiDou short message transmission in areas without public network coverage. In this application, the communication module is responsible for sending the encapsulated source data packets to the access nodes of the blockchain network, completing the data transfer from the physical world to the digital ledger.

[0035] Through the above technical solution, this application achieves the dual proof capability of source data packets having spatiotemporal non-repudiation and device identity non-forgeability by integrating Beidou / GNSS positioning modules and security chips at the data source and strongly binding business data, spatiotemporal stamps and device fingerprints through hash operations. This solves the technical problem in traditional traceability systems where the data source is easily forged and it is difficult to trace the true responsible party afterward, thus achieving the technical effect of constructing a highly reliable digital archive of tobacco leaves throughout their entire life cycle.

[0036] Furthermore, the operating mechanism of the multi-chain collaborative blockchain network includes: The production alliance subchain consists of multiple entity nodes in the tobacco industry chain. It adopts a practical Byzantine fault-tolerant consensus algorithm to store full and detailed production process data. Data association smart contracts and regulatory summary generation smart contracts are deployed on the production alliance subchain. The regulatory main chain consists of at least one industry regulatory agency node, which is used to store key regulatory summary data and violation evidence hash values ​​generated by the regulatory summary generation smart contract and synchronized through the cross-chain protocol.

[0037] Specifically, the production alliance subchain can refer to a distributed ledger network jointly maintained by multiple business-related entities in the tobacco industry chain. The node composition of this production alliance subchain can be diverse, including nodes from planting cooperatives, curing plants, re-drying plants, and cigarette manufacturing enterprises, which together constitute an autonomous alliance in the tobacco production process. This application embodiment does not impose specific limitations on the number and type of nodes, which can be set according to the actual industry scale and organizational structure. Functionally, this production alliance subchain primarily undertakes the task of storing full and detailed production process data, ensuring that every agricultural operation record (such as fertilization, spraying, and harvesting) is completely and immutably recorded on the chain. To achieve efficient data consensus, the production alliance subchain can adopt the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The PBFT algorithm can guarantee strong consistency with low latency even in the presence of a small number of malicious nodes, making it very suitable for consortium blockchain scenarios with a relatively fixed number of nodes and high throughput requirements. On this production alliance subchain, data association smart contracts and regulatory summary generation smart contracts are also deployed. The data association smart contract can be used to monitor and automatically connect discrete agricultural event data to form logically rigorous batch archives; the regulatory summary generation smart contract is used to extract key information that meets regulatory requirements from massive amounts of production data. These two smart contracts work closely with other technical features of the production alliance subchain, enabling data to automatically complete logical aggregation and regulatory screening after entering the subchain, providing a structured data foundation for subsequent cross-chain synchronization.

[0038] The regulatory main chain can refer to a blockchain network primarily built by an industry regulatory body with higher credibility and regulatory authority. The node structure of this regulatory main chain is relatively streamlined, and may include nodes from national-level tobacco monopoly bureaus, provincial-level tobacco monopoly bureaus, or other legally authorized regulatory bodies. Unlike the production alliance sub-chain, the regulatory main chain does not store all detailed production data, but focuses on storing key regulatory summary data and hash values ​​of evidence of violations. This design allows the regulatory main chain to effectively regulate tobacco quality and safety while protecting the commercial privacy of enterprises (such as specific formulas and detailed process parameters). The data stored on the regulatory main chain originates from the production alliance sub-chain, specifically generated by a regulatory summary generation smart contract deployed on the production alliance sub-chain and synchronized via a cross-chain protocol. Key regulatory summary data can refer to semantically refined risk indicators or compliance proofs, such as conclusive data like a batch of tobacco leaves passing pesticide residue testing or a plot of land meeting operational standards, along with their corresponding original transaction Merkle root data; the hash value of evidence of violations refers to an encrypted index pointing to the original violation record generated when abnormal data is detected. The regulatory main chain, through its collaboration with the production alliance sub-chains, achieves a governance model where data is available but not visible: regulators can identify risk clues and verify their authenticity through the regulatory main chain, but they do not have the right to directly access the full set of detailed data stored on the production alliance sub-chains, thus balancing the contradiction between regulatory transparency and data sovereignty.

[0039] The working process and principle of the multi-chain collaborative blockchain network in this application are as follows: First, the source data packets collected by the IoT data anchoring terminal are broadcast to the production alliance sub-chain. Multiple entity nodes on the chain use the PBFT consensus algorithm to quickly verify and package the data for storage, forming an immutable full production ledger. Subsequently, the regulatory summary generation smart contract deployed on the production alliance sub-chain runs automatically, filtering key regulatory information from the full data according to preset rules, and generating regulatory summaries and corresponding cryptographic proofs. Next, these summary data and proofs are synchronized to the regulatory main chain through a cross-chain protocol, where regulatory agency nodes perform secondary storage. Finally, when quality traceability or regulatory verification is required, the querying party can first obtain high-level regulatory summaries and verification proofs from the regulatory main chain. If further verification is required, the querying party can then apply for Merkel proofs of specific detailed data from the production alliance sub-chain based on the verification proofs, thereby achieving a trusted closed loop from macro-level supervision to micro-level traceability.

[0040] Through the above technical solution, this application achieves the following beneficial effects: Because it employs a production alliance sub-chain composed of multiple entity nodes running the PBFT consensus algorithm, it can process massive amounts of detailed production process data with high throughput and low latency, ensuring the integrity and real-time nature of industry data; Because it constructs a regulatory main chain that only stores key regulatory summaries and hash values ​​of violation evidence, and collaborates with the production alliance sub-chain through a cross-chain protocol, it can achieve penetrating verification of key quality indicators by regulatory agencies without disclosing corporate trade secrets and full-scale detailed data, effectively resolving the structural contradiction between full-scale evidence storage and precise regulation; Because it deploys data association smart contracts and regulatory summary generation smart contracts on the production alliance sub-chain, it achieves automated processing from raw data upload to regulatory summary extraction, significantly reducing the cost of manual intervention and improving the intelligence level and credibility of the traceability system.

[0041] Furthermore, the multi-chain collaborative blockchain network also includes a cross-chain relay component; The cross-chain protocol is configured as follows: the cross-chain relay component receives a cross-chain request submitted by the regulatory summary generation smart contract, which contains the key regulatory summary data and the original transaction Merkel proof; after verifying the validity of the Merkel proof, the key regulatory summary data is packaged into a new block of the regulatory main chain, and a regulatory evidence hash is generated. The main regulatory chain can verify the authenticity of the evidence data stored on the production alliance sub-chain based on the regulatory evidence hash and the Merkel proof, but it does not have the right to directly access the full amount of detailed data stored on the production alliance sub-chain.

[0042] Specifically, the cross-chain relay component can refer to an independent communication and verification node deployed between the production consortium subchain and the regulatory main chain, or it can refer to a collection of software modules running a lightweight blockchain protocol. The cross-chain relay component functions as a trust bridge connecting two heterogeneous or homogeneous blockchains in the overall technical solution. Its role is to receive cross-chain requests from the production consortium subchain and independently verify the cryptographic proofs carried in the requests, thereby ensuring that only genuine and valid data digests can be synchronized to the regulatory main chain. The cross-chain relay component works in conjunction with the regulatory digest generation smart contract. The regulatory digest generation smart contract is responsible for extracting key data and generating proofs on the subchain side, while the cross-chain relay component is responsible for verifying the validity of the proofs at the intermediate layer of the link and delivering the results to the regulatory main chain. Through this cooperation, the secure flow of regulatory data from the subchain to the main chain is achieved. The result is the generation of a cryptographically endorsed regulatory evidence hash on the regulatory main chain without exposing the original business details on the subchain.

[0043] A cross-chain protocol can refer to a set of communication specifications that define cross-chain message formats, verification logic, and state update rules. The current functional positioning of this cross-chain protocol is to regulate the behavior of cross-chain relay components, ensuring the trustworthiness and consistency of the cross-chain process. This cross-chain protocol has a close logical connection with the original transaction Merkel proof. The protocol stipulates that the Merkel proof must be carried as evidence of data existence. The cross-chain relay component compares the hash path of the Merkel proof according to the protocol, confirming that the proof indeed points to a specific transaction in a valid block on the production consortium's sub-chain. Through this protocol-based verification mechanism, it is ensured that the key regulatory summary data transmitted to the regulatory main chain has not been tampered with and truly originates from the sub-chain. The result is the construction of a closed-loop verification system where data is usable but not visible.

[0044] Key regulatory summary data can refer to aggregated information or key indicators that meet regulatory requirements and are extracted from the full data of the production consortium's subchains, such as pesticide residue test results for a specific batch, hashes of violation records, or root hashes of batch files. In the current scheme, this key regulatory summary data serves as the core object for evidence storage on the regulatory main chain. Its data volume is much smaller than the full data of the subchains, facilitating efficient storage and retrieval on the main chain. This key regulatory summary data, together with the Merkel proof of the original transaction, constitutes the payload of the cross-chain request. The Merkel proof is used to prove the authenticity of the summary data's source on the subchain. Together, they allow the regulatory main chain to be confident in the credibility of the summary data even without obtaining details from the subchain. The resulting work is that the verified regulatory conclusions and their corresponding cryptographic anchors are recorded in the regulatory main chain blocks.

[0045] An original transaction Merkle proof can refer to a set of path hashes generated based on a Merkle tree structure to prove that a transaction exists in a specific block. The function of this original transaction Merkle proof is to provide a concise and unforgeable proof of existence. Its name comes from the root hash property of the Merkle tree: any change to a leaf node (i.e., the original transaction) will cause a change in the root hash. This original transaction Merkle proof has a verification-and-verification relationship with the cross-chain relay component. The cross-chain relay component compares the path hashes in this proof with the Merkle root in the block header publicly disclosed by the production consortium sub-chain. If the comparison matches, it proves that the original transaction does indeed exist in the sub-chain and has not been tampered with. The result of this collaboration is that the cross-chain relay component confirms the legitimacy of the cross-chain request and allows the corresponding key regulatory summary data to be packaged and uploaded to the chain.

[0046] The regulatory evidence hash refers to the globally unique hash value generated by the regulatory main chain when it receives key regulatory summary data verified by the cross-chain relay component and packages it into a new block. In the current technical solution, this regulatory evidence hash serves as the final index and integrity verification code for the regulatory data. There is a linked verification relationship between this regulatory evidence hash and Merkel proofs. When a third party needs to verify the authenticity of regulatory data, it can use the regulatory evidence hash to locate the specific summary data on the regulatory main chain and, combined with the original transaction Merkel proof, trace back to the original transaction on the production consortium sub-chain. Through this dual hash anchoring mechanism, non-repudiable evidence of key regulatory information throughout the entire lifecycle of tobacco leaves is achieved. The result is that any tampering with this regulatory data will result in a hash value mismatch and will be immediately detected.

[0047] Through the above technical solution, this application achieves the goal of enabling the regulatory main chain to independently verify the authenticity and integrity of key regulatory summary data without directly accessing the full detailed data of the production alliance sub-chains, thanks to the introduction of cross-chain relay components and cross-chain protocols based on Merkel proofs. This solves the technical problem of the difficulty in balancing privacy protection and penetrating supervision in traditional multi-chain architectures, and achieves the technical effect of ensuring that regulatory conclusions have judicial admissibility while strictly isolating the company's commercially sensitive data.

[0048] Furthermore, the data-associated smart contract is configured to execute the following logic: Continuously monitor and receive multiple source data packets that are stored on the production alliance subchain and carry the same geographic unit identifier or the same batch code; According to the time order of the BeiDou time and space stamps in each source data packet, the source data packets representing discrete agricultural events are automatically linked together into an event chain with a temporal logical relationship. In response to receiving a source data packet from the harvesting stage that carries a specific batch code, the system automatically digitally binds the batch code to all concatenated source data packets belonging to the same production unit, generating and storing a complete digital batch file.

[0049] Specifically, continuous monitoring and reception refers to the data-associated smart contract being permanently running on the production alliance subchain, scanning newly packaged transaction data onto the chain in real time through an event-driven mechanism. This function is positioned as the entry point for data aggregation, ensuring that any new agricultural event data is captured by the contract as soon as it enters the subchain. Its cooperation with the production alliance subchain manifests as follows: the contract node, as part of the subchain network, directly reads the transaction payload within the block body, filtering out source data packets carrying specific fields (such as geographical unit identifiers or batch codes). Through this cooperation, the system automatically identifies and extracts discrete data fragments belonging to the same logical dimension from massive amounts of on-chain data.

[0050] The same geographic unit identifier or the same batch code can refer to a unique key value used to logically divide the data's belonging range. The geographic unit identifier can be set according to the actual situation, for example, it can be a grid code based on BeiDou coordinates (such as plot A01), or a logical ID generated based on administrative region or planting cooperative number; the batch code can be a specific string entered by the user during the harvesting process (such as BATCH2025082001). The role of this technical feature in the overall solution is to provide a basis for clustering. Its linkage with other technical features is that when the identifier field in the source data packet matches the contract's preset filtering conditions, it triggers the subsequent data concatenation logic, thereby logically aggregating data that is physically scattered across different blocks and uploaded by different terminals into the same virtual container.

[0051] The order of BeiDou spatiotemporal stamps in each source data packet can be determined by using the precise timestamps generated by IoT data anchoring terminals and encapsulated within the source data packets as sorting keys. BeiDou spatiotemporal stamps not only include date and time but are also accurate to the millisecond level, ensuring the uniqueness and immutability of the time dimension. This technical feature, in conjunction with discrete agricultural events, manifests as follows: the contract reads the BeiDou spatiotemporal stamps of all source data packets within the same geographical unit or batch and executes an ascending order sorting algorithm. Its functional role in the technical solution is to reconstruct the causal chain of agricultural activities. For example, it can restore previously isolated operation records such as seedling cultivation, transplanting, fertilization, and pesticide application into a linear sequence of operations based on their actual occurrence time. Through this coordination, an event chain with strict temporal logic is formed, eliminating potential time inversions or logical inconsistencies caused by manual recording.

[0052] An event chain with temporal logical relationships can refer to a linked list structure consisting of multiple source data packet reference pointers or hash values ​​arranged along a timeline. This event chain itself does not directly store the original large file; instead, it stores indexes pointing to the storage locations of each source data packet on the blockchain and their hash digests to ensure verification efficiency. Its name derives from its ability to reflect the continuity and logic of agricultural operations during tobacco leaf growth. The linkage between this technology and the harvesting stage lies in its role as a prerequisite for generating the final archive; it can only respond to subsequent binding requests when the event chain is fully constructed and uninterrupted. Its result is a dynamically updated, traceable historical operation trajectory; any attempt to insert false time nodes will be rejected due to failure to pass the time continuity verification.

[0053] Receiving a source data packet carrying a specific batch code from the harvesting stage can refer to a pre-set trigger logic within the data-associated smart contract. The harvesting stage is typically considered a key milestone in a production cycle, and the batch code carried in the source data packet it generates has a sealing attribute. This technical feature functions as a start signal for archive generation. Its relationship with the event chain is as follows: once the contract parses the harvesting data packet with the batch code, it immediately suspends the regular sorting of other non-critical data under that geographic unit and activates the archiving process. This mechanism ensures that all previous historical data is solidified only when the production cycle reaches a specific node (harvesting), avoiding the problem of premature archive generation preventing the inclusion of subsequent data.

[0054] Automatically binding the batch code to all concatenated source data packets belonging to the same production unit can involve performing cryptographic hash operations or a Merkle tree construction process, using the batch code as the root node or key leaf node, and combining it with the hash values ​​of all data packets in the previously generated event chain. The digital binding can be implemented by generating a new composite hash value that depends on the batch code and the hash value of each link in the event chain; any tampering with historical links will result in a change to the final bound value. This technical feature plays a crucial role in achieving data indivisibility. Its linkage with generation and storage is as follows: after the binding operation is completed, the contract automatically calls the storage interface to write the generated complete digital batch file into the state database of the production alliance sub-chain. Through this combination, a single data object with cryptographic integrity protection is formed, covering the entire lifecycle from planting to harvesting.

[0055] A complete digital batch archive can refer to a structured collection containing reliable data on the entire process of a batch of tobacco leaves from source to harvest. Its content format can be set according to actual needs; for example, it can be a collection of metadata in JSON format or a binary large object storage reference. This archive not only records what was done but also proves when, where, and by whom it was done through built-in spatiotemporal stamps and device fingerprints. Its role in the system is as the basic data unit for subsequent cross-chain synchronization and query verification. This application does not impose special limitations on this; the specific data structure of the archive only needs to reflect temporal logic and binding relationships.

[0056] The working process and principle of this application are as follows: After the data association smart contract is deployed on the production alliance sub-chain, it continuously monitors newly generated blocks on the chain. When a new source data packet is detected on the chain, the contract first parses its payload and extracts the geographical unit identifier (such as the plot ID) or temporary batch marker. If other source data packets already exist under this identifier, the contract retrieves these historical data, reads the BeiDou spatiotemporal stamps, and links them into a dynamic event chain in chronological order. During this process, if new agricultural operations (such as fertilization or spraying) occur and are uploaded to the chain, the event chain will automatically extend. Once the contract detects a source data packet marked as harvest and carrying a formal batch code, it immediately determines that the current planting cycle of the production unit has ended. At this time, the contract locks the current event chain, merges the formal batch code with the hash values ​​of all historical nodes on the chain, generates a unique digital batch file hash, and permanently stores this file on the sub-chain. This process is completely executed automatically by the code without human intervention, ensuring the rigor and objectivity of the traceability logic.

[0057] Through the above technical solutions, this application has achieved the technical problems of chain breakage, time logic confusion and unclear data ownership caused by the reliance on manual post-event data processing in traditional traceability systems by introducing an automatic sorting mechanism based on Beidou spatiotemporal stamps and digital binding logic based on harvest triggers. This has resulted in the automatic generation of logically rigorous and tamper-proof full lifecycle digital batch archives, realizing the technical effect of intelligent transformation from discrete agricultural events to complete traceability archives.

[0058] Furthermore, before the data-associated smart contract executes the listening and receiving logic, it also includes: Parse the geographic coordinates contained in the BeiDou time and space stamp in each source data packet, determine whether the geographic coordinates fall within one or more predefined geofences, and associate the source data packets that are determined to fall within the same geofence with the same geographic unit identifier.

[0059] Specifically, parsing the geographic coordinates contained in the BeiDou spatiotemporal stamps in each source data packet can refer to the data preprocessing operation first performed by the data-associated smart contract after receiving the source data packet from the IoT data anchoring terminal. These geographic coordinates can be latitude and longitude information obtained through the BeiDou / GNSS positioning module, such as longitude and latitude values ​​in the WGS-84 coordinate system, or coordinates in a specific projected coordinate system converted according to project requirements. This application embodiment does not impose any special limitations on this. The function of this technical feature is to transform the abstract spatiotemporal information carried in the source data packet into specific spatial location parameters, serving as the input basis for subsequent spatial matching calculations. In the system linkage relationship, these geographic coordinates originate from the source data packet generated in the preceding steps, and their accuracy directly depends on the trusted spatiotemporal stamp generated by the BeiDou / GNSS positioning module and security chip integrated in the IoT data anchoring terminal, ensuring the authenticity and immutability of the source location information.

[0060] Determining whether a geographic coordinate falls within one or more predefined geofences can refer to the process of comparing the parsed real-time geographic coordinates with a pre-stored geofence database in the system using spatial geometric algorithms. The geofence can be a polygonal, circular, or other arbitrarily shaped closed area defined based on the actual boundaries of the tobacco planting plot. It can be set according to the actual plot distribution; for example, it can be a circular fence generated based on the plot's center point and radius, or a polygonal fence generated based on the plot's four boundary coordinates. This application does not impose any special limitations on this. The functional positioning of this technical feature in the overall technical solution is to achieve automatic mapping between physical world plots and digital world identifiers. In conjunction with other technical features, this judgment logic operates within the data-associated smart contract. Its input is the parsed geographic coordinates, and its output is a Boolean judgment result (i.e., falling within or not falling within), which determines whether the current source data packet is eligible to enter the subsequent data aggregation process.

[0061] Associating source data packets identified as falling within the same geofence with the same geographic unit identifier can refer to the process where, upon confirmation of inclusion, the smart contract automatically assigns or binds a unique identifier representing the geofence to the source data packet. The geographic unit identifier can be a code string used to distinguish different planting plots, production units, or management areas, such as plot A01 or field B-2025-03. Specific encoding rules can be set according to actual conditions, and this application does not impose any special limitations on this. The result of this technical feature, achieved through the above-described collaboration, is that source data packets for all discrete agricultural events (such as fertilization, spraying, and irrigation) generated within the same physical plot are automatically tagged with the same geographic unit identifier, regardless of their generation time or operation type. This mechanism enables the system to automatically complete preliminary data clustering based on spatial location attributes without manual intervention, providing a unified spatial dimension index foundation for subsequently connecting events in chronological order to form a complete event chain.

[0062] Through the above technical solution, this application achieves the automatic transformation of abstract latitude and longitude data into specific land parcel management identifiers by adding an automatic geofence judgment mechanism based on Beidou geographic coordinates to the front end of the data association logic. This solves the technical problems of traditional traceability systems that rely on manual entry of land parcel information, which is prone to errors and falsification, and is difficult to achieve refined spatial management. Thus, without increasing the cost of manual operation, it ensures that all agricultural data within the same physical production unit can be accurately and automatically collected and associated, significantly improving the spatial dimension accuracy and management granularity of tobacco leaf traceability data throughout its entire life cycle.

[0063] Furthermore, in the source data packet generated by the IoT data anchoring terminal, the BeiDou spatiotemporal stamp and the device digital fingerprint together constitute two-factor anchoring information, which is used to provide dual proof of spatiotemporal non-repudiation and device identity non-forgeability for the business data. The device digital fingerprint is generated by combining a session random number generated by the true random number generator of the security chip with the device ID to ensure that each generated fingerprint is unique.

[0064] Specifically, two-factor anchoring information refers to a composite verification data structure composed of two independent yet complementary elements: a BeiDou spatiotemporal stamp and a device digital fingerprint. The BeiDou spatiotemporal stamp provides the absolute time and spatial coordinates of the data's creation, solving the "when and where" problem; while the device digital fingerprint provides the physical carrier identity of the data's source, solving the "who created" problem. These two elements work synergistically in the source data packet to form a strongly bound chain of evidence: any attempt to tamper with business data will fail the hash verification if a matching spatiotemporal trajectory and corresponding device dynamic identity are not simultaneously forged. This dual-proof mechanism ensures that the source data packet possesses spatiotemporal non-repudiation and unforgeable device identity, meaning attackers cannot replay historical data or impersonate legitimate terminals to inject false data without being detected.

[0065] A device digital fingerprint can refer to a dynamically changing, cryptographically strong identification string. Its generation is based on a security chip deployed inside the IoT data anchoring terminal. This security chip integrates a True Random Number Generator (TRNG), which uses internal physical noise (such as thermal noise, metastability, etc.) to generate a high-entropy random bit stream. Upon each critical event trigger, the TRNG generates a session random number in real time, valid only for the current session. This session random number is then cryptographically combined with a unique, pre-programmed, and unchangeable device ID in the security chip (e.g., through the HMAC algorithm or specific hash concatenation logic) to generate a unique device digital fingerprint for that session. Because the session random number is unpredictable and one-time, even if an attacker intercepts the device digital fingerprint from a single communication, they cannot deduce the fingerprint for the next session, thus completely eliminating the risk of replay and cloning attacks.

[0066] In existing tobacco quality traceability systems, centralized databases are commonly used to store manually entered information at each stage. This model has significant technical drawbacks: First, the data source lacks effective anti-tampering measures, and manual entry is susceptible to subjective factors, leading to frequent falsification at the source and making it difficult to guarantee the authenticity and non-repudiation of the data. Second, the single centralized storage architecture is prone to single-point-of-failure risks; if the server is attacked or experiences hardware failure, historical traceability data can easily be lost or maliciously modified. Third, traditional traceability logic is mostly based on manual post-event correlation, resulting in a fragile and rigid chain that cannot automatically connect discrete agricultural events into a complete lifecycle archive, failing to meet the dual demands of multiple stakeholders for data privacy protection and regulatory transparency. Furthermore, existing technologies often only focus on the secure transmission of location information or simple blockchain notarization, failing to form a complete and reliable traceability solution that deeply integrates BeiDou spatiotemporal services and hardware security modules, covering the entire lifecycle from field planting to finished product processing.

[0067] Based on the above issues, please refer to Figure 5 The flowchart of a method for reliable traceability of tobacco leaf quality throughout its entire life cycle, provided in this application embodiment, includes the following steps: S101, Data source anchoring: When key events occur in each tobacco production process, the IoT data anchoring terminal collects business data in real time and adds a Beidou time and space stamp and device digital fingerprint to the business data to generate a trusted source data packet. S102, Sub-chain consensus notarization: The trusted source data packet is broadcast to the production alliance sub-chain network. After consensus verification by multiple nodes, it is packaged and uploaded to the chain to form an immutable notarization record. S103. Smart contract aggregation: Data-associated smart contracts deployed on the production alliance sub-chain continuously aggregate the evidence records corresponding to multiple discrete events belonging to the same production unit or the same batch into logically associated batch files. S104. Cross-chain regulatory synchronization: The regulatory summary generation smart contract extracts key regulatory summary information from the batch files and synchronizes the key regulatory summary information to the regulatory main chain for evidence storage through the cross-chain protocol. S105, Multi-role Trusted Query: Receives and responds to query requests from different roles through the trusted query service platform, and returns blockchain-backed traceability information or encrypted proof of data integrity based on query permissions.

[0068] Through the synergistic effect of the above five steps, a reliable traceability closed loop spanning the entire lifecycle of tobacco leaves is constructed. The first step utilizes a two-factor anchoring mechanism combining BeiDou spatiotemporal stamps and device digital fingerprints to ensure the authenticity and non-repudiation of business data from the physical source. The second step uses consensus-based notarization through the production alliance sub-chain, leveraging distributed ledger technology to guarantee data tamper-proofing during transmission and storage. The third step utilizes the automated logic of smart contracts to dynamically aggregate discrete agricultural events into logically rigorous batch archives, overcoming the lag and error-proneness of traditional manual correlation. The fourth step achieves the isolation and connection between production data and regulatory data through cross-chain protocols, meeting the government's need for penetrating supervision while protecting commercial privacy. The fifth step transforms the technological trust on the blockchain into business value that is understandable and verifiable for different roles through tiered query services. Each step is interconnected, with the output of the previous step serving as the input for the next, collectively achieving full-process automation and reliability from data collection, notarization, aggregation, supervision to query, significantly improving the transparency, efficiency, and credibility of the tobacco leaf quality traceability system.

[0069] According to an embodiment of this application, preferably, the batch files dynamically aggregated into logical associations include: The data association smart contract parses the BeiDou time and space stamp in each trusted source data packet and extracts its geographical coordinates; Call the pre-configured geofence database to determine the parcel identifier to which the geographic coordinates belong; Trusted source data packets with the same plot identifier but different operation types are sorted and linked according to the time sequence of their BeiDou spatiotemporal stamps to form a time-series agricultural operation chain for that plot. When a trusted source data packet carrying a batch code is received during the harvesting process, the entire time-series agricultural operation chain is hashed and associated with the batch code to generate an indivisible digital batch file.

[0070] Through the synergistic effect of the above steps, automated and intelligent aggregation of tobacco production data was achieved. Specifically, by parsing BeiDou spatiotemporal stamps to extract geographic coordinates, precise spatial attributes were assigned to the data; combined with a pre-configured geofence database, automatic aggregation of data to physical plots was achieved, avoiding errors from manual division; furthermore, timestamp sorting was used to reconstruct discrete events within the same plot into a continuous time-series operation chain, restoring the true production process; finally, batch coding in the harvesting stage triggered hash associations, stitching together long-cycle production process data into indivisible digital batch archives. This series of interconnected steps not only solved the problems of data silos and logical breaks in traditional traceability systems, but also ensured that each generated digital archive possessed the characteristics of traceable origin, traceable destination, and accountable responsibility, providing a solid data foundation for subsequent cross-chain regulatory synchronization and multi-role trusted queries.

[0071] According to an embodiment of this application, preferably, the step of returning the blockchain-backed traceability information or encrypted proof of data integrity based on query permissions includes: In response to a lightweight query request from a consumer terminal, the trusted query service platform retrieves and returns a key information graph corresponding to the query identifier from the regulatory main chain or the production alliance sub-chain. The key information graph includes at least a visual summary of the place of origin, harvest time, and major agricultural records. In response to a deep verification request from the regulator's terminal, the trusted query service platform calls the cross-chain verification interface to obtain the regulatory evidence hash and cross-chain verification proof corresponding to the target batch from the main regulatory chain, and to obtain the transaction hash and Merkel proof corresponding to the specific agricultural event from the production alliance sub-chain. The platform then generates and returns a comprehensive verification report containing multi-layered encrypted verification results, which can be verified by an independent blockchain explorer.

[0072] Through the aforementioned dual-channel query mechanism, precise services and trust transfer are achieved to meet the needs of different roles. By distinguishing between lightweight query requests from consumer terminals and deep verification requests from regulator terminals, the system can flexibly allocate on-chain resources: for general consumers, the pre-aggregated key information graph provides a fast and intuitive traceability experience, lowering the threshold for public participation in supervision; for professional regulators, the cross-chain verification interface deeply integrates the authoritative endorsement of the regulatory main chain with the detailed evidence of the production alliance sub-chain, constructing a full-link verification closed loop from macro-summary to micro-transaction. The synergistic effect of the key information graph and the penetrating verification report is that the former serves as the entry point of trust, guiding users to establish initial understanding, while the latter serves as the cornerstone of trust, ensuring the absolute authenticity of the data source and the complete traceability of the flow process through multiple cryptographic anchors such as regulatory evidence hashes, cross-chain verification proofs, transaction hashes, and Merkel proofs. In particular, the characteristic that the penetrating verification report can be verified by an independent blockchain explorer completely eliminates the dependence on centralized query platforms, making the traceability results have judicial evidentiary effect, truly achieving absolute trust in a zero-trust environment.

[0073] To better understand this application, further explanations are provided below in conjunction with the accompanying drawings: Figure 1 This is a schematic diagram of the overall architecture of the trusted traceability system for the entire life cycle quality of tobacco leaves based on BeiDou spatiotemporal anchoring and blockchain consensus, as described in this application. Figure 1 As shown, the system consists of four core layers: the physical world and data source layer A, the data anchoring and on-chain layer B, the blockchain storage and collaboration layer C, and the application and service layer D. The information flow and data flow between each layer constitute a complete trusted traceability system.

[0074] In the physical world and data source layer A, there are: intelligent equipment A1, such as bud suppressing machines, plant protection machines, harvesting machines, and intelligent curing barns, which generate raw business data during operation; environmental sensing nodes A2, field intelligent sensing nodes that continuously collect environmental and crop growth data; and management systems A3, including growth decision-making systems and curing process control systems, which generate decision-making instructions and process parameters. These data sources together constitute the raw data foundation for the entire life cycle of tobacco leaves.

[0075] The data anchoring and on-chain layer B includes: IoT data anchoring terminal B1, deployed on various smart equipment and key nodes, each terminal integrating a Beidou / GNSS positioning module and a security chip; a trusted data packet generation module B2, which automatically adds a Beidou spatiotemporal stamp accurate to milliseconds, centimeter / meter-level positioning coordinates, and a unique cryptographic identifier based on the security chip to the original business data each time a key event occurs, and the three are hashed together to form a trusted data packet; and a data broadcasting module B3, which broadcasts the generated trusted data packet to the blockchain network nodes through 4G / 5G or wired networks.

[0076] The blockchain evidence storage and collaboration layer C includes: a production alliance sub-chain C1, jointly maintained by industry entities such as planting cooperatives, drying workshops, and re-drying plants as nodes, employing efficient consensus algorithms such as PBFT, responsible for storing full and detailed production process data; smart contract 1, a data association contract C2, deployed on the production alliance sub-chain, continuously monitoring newly added data packets and automatically establishing logical associations between data based on land parcel codes and batch numbers; smart contract 2, a regulatory summary generation contract C3, periodically extracting key summary information that meets regulatory requirements from the production sub-chain; a cross-chain relay C4, which securely synchronizes the generated regulatory summaries and key evidence hashes to the regulatory main chain through hash locking or relay chain technology; and a regulatory main chain C5, maintained by tobacco industry regulatory agencies as core nodes, storing only key summary data and violation evidence hashes, supporting penetration verification but not accessing detailed enterprise data.

[0077] The application and service layer D includes: a trusted query service platform D1, which provides a unified query interface; a consumer QR code query platform D2, where consumers can scan the QR code on cigarette packs to obtain a key information graph verified by the blockchain; a regulator deep verification platform D3, where regulators can input batch numbers or clues of violations to obtain encrypted proof of data integrity and that it has not been tampered with; and a producer data dashboard D4, where producers can view the status and traceability records of all on-chain data of their enterprise.

[0078] Figure 1 The solid line with arrows indicates the main direction of the data flow: raw data is generated from various equipment and systems in the physical world and data source layer A, flows to the anchoring terminal in the data anchoring and on-chain layer B for trust processing, then is broadcast into the various levels of on-chain storage in the blockchain notarization and collaboration layer C, and finally provides query services to various users D2, D3, and D4 through the trusted query service platform D1. The dashed line with arrows indicates the feedback path and knowledge interaction: the regulatory main chain C5 can initiate verification requests to the production alliance sub-chain C1, but does not obtain detailed data; the producer data dashboard D4 can retrieve the statistical analysis results of its own enterprise's data.

[0079] Figure 2 is a flowchart illustrating the internal module structure and data packaging process of the IoT data anchoring terminal B1 in this invention, detailing the entire process from raw data acquisition to the generation of trusted data packets. Figure 2 contains two sub-figures: Figure 2a A diagram showing the hardware module configuration of the anchoring terminal. Figure 2b A timeline flowchart for data packaging.

[0080] like Figure 2aAs shown, the IoT data anchoring terminal B1 includes the following hardware modules: a main control unit 201, which uses an industrial-grade ARM processor and runs an embedded operating system and anchoring program; a Beidou / GNSS positioning module 202, which supports Beidou-3 system signal reception and can output centimeter-level RTK positioning data and nanosecond-level time synchronization signals. The module is equipped with a dedicated antenna 2021 to ensure signal reception quality; a security chip 203, which is a national cryptographic level 2 certified security chip, with a built-in unique and tamper-proof device ID 204, and provides hardware-level encryption computing capabilities for generating device digital fingerprints and signatures; and a data interface unit 205, including... The system features multiple interfaces including RS232 / 485, CAN bus, and Ethernet, used for data exchange with connected intelligent equipment and receiving raw business data. Storage unit 206 includes RAM and Flash memory chips for temporarily caching data packets to be uploaded to the blockchain and summary information of already uploaded data. Communication unit 207 supports dual-mode switching between 4G / 5G cellular network communication and BeiDou short message communication, ensuring that critical data can still be transmitted via BeiDou short messages even in areas without public network signals. Power management unit 208 provides stable power to each module and supports wide voltage input to adapt to the power supply environment of field equipment.

[0081] Figure 2b The sequence diagram for data packaging illustrates the complete process from event triggering to data packet generation: Step ① Event Trigger: When the connected intelligent equipment performs a key operation, such as the start of operation of the bud suppressor or the harvester harvesting a tobacco leaf, the equipment control system sends a trigger signal and raw business data 209 to the anchor terminal through the data interface unit 205, including the operation type, operation parameters, and operation object identifier.

[0082] Step ② Obtain the BeiDou time and space stamp: The main control unit 201 immediately calls the BeiDou / GNSS positioning module 202 to obtain the current precise UTC time accurate to milliseconds and the positioning coordinates (latitude and longitude), with an accuracy of up to the centimeter level, and generates a BeiDou time and space stamp data packet 210. The time and space stamp includes a timestamp T, longitude Lon, latitude Lat, and a positioning quality factor Q to indicate the positioning accuracy level.

[0083] Step 3: Read the device digital fingerprint: The main control unit 201 reads the unique device ID 204 stored in the security chip 203 through the security chip interface and generates the device digital fingerprint 211. The digital fingerprint contains the device ID and a session random number generated based on the chip's true random number generator to ensure the uniqueness of each signature.

[0084] Step 4: Data Hash Calculation: The main control unit 201 concatenates the original business data 209, the BeiDou time stamp 210, and the device digital fingerprint 211, and calls a hash algorithm such as SHA-256 to generate a fixed-length digital digest 212. The digital digest is equivalent to the "fingerprint" of the data packet; any slight modification to the original data will cause the digest value to change completely.

[0085] Step ⑤ Data packet encapsulation and local storage: Encapsulate the original business data 209, Beidou time stamp 210, device digital fingerprint 211, digital digest 212 and timestamp into a complete data packet 213 and store it in the local storage unit 206 for future reference.

[0086] Step 6: Triggering On-Chain Access: After encapsulation, the main control unit triggers data upload through communication unit 207, sending data packet 213 to the designated blockchain node for consensus and evidence storage.

[0087] In Figure 2, solid lines with arrows indicate the flow of data and control signals, while dashed lines represent the connections between modules. This design, using anchored terminals, ensures that every piece of on-chain data possesses unforgeable spatiotemporal attributes and device identity, thus addressing the data trust issue at its source.

[0088] Figure 3 is a schematic diagram of the data synchronization and cross-chain verification mechanism of the multi-chain collaborative blockchain network in this invention, illustrating the collaborative relationship between the production alliance sub-chain and the regulatory main chain. Figure 3 contains two sub-diagrams: Figure 3a This is a network topology diagram of multi-chain collaboration. Figure 3b A timeline diagram for cross-chain data synchronization.

[0089] like Figure 3a As shown, the multi-chain collaborative network consists of the following nodes and links: the production alliance sub-chain C1 is composed of multiple industry nodes, including planting cooperative node 301, baking factory node 302, re-drying factory node 303, etc., which jointly maintain the complete ledger of the sub-chain. Each industry node deploys full data storage 304, storing all refined production process data related to that node. The sub-chain adopts the PBFT consensus algorithm. When a trusted data packet uploaded by the anchor terminal is received, all nodes jointly verify and package it into a block. Each block contains a block header 305 and a block body 306. The block header records the hash of the previous block, Merkle root, timestamp, etc., and the block body contains multiple verified transaction data.

[0090] The regulatory main chain C5 consists of regulatory agency nodes (307), including the national tobacco monopoly bureau node (3071) and provincial regulatory nodes (3072), etc. The main chain only stores key regulatory summary data (308) and does not store full detailed data of industry nodes.

[0091] The cross-chain relay C4 is a core component for achieving dual-chain collaboration, comprising: a relay chain node 309, running a lightweight blockchain protocol specifically for handling cross-chain messages; a validator set 310, composed of validators jointly elected by industry nodes and regulatory nodes, responsible for verifying the validity of cross-chain transactions; a hash-locked contract 311, deployed on the relay chain, used to achieve atomic swaps of cross-chain assets or data; and a Merkel proof validator 312, used to verify the validity of Merkel proofs from the source chain.

[0092] Figure 3b The timeline diagram for cross-chain data synchronization illustrates the complete process from data generation on the production subchain to the main regulatory chain receiving the digest: Step ① Data Upload: The trusted data packet uploaded by the anchor terminal is packaged into a block after consensus is reached on the production alliance sub-chain C1, generating the transaction hash TX001 and the data storage location.

[0093] Step ② Triggering Regulatory Summary Generation: The smart contract 2C3 deployed on the production subchain listens for the generation of a new block and determines whether a regulatory summary needs to be generated based on preset rules, such as the involvement of specific plots of land or the use of specific pesticides. If necessary, the contract automatically extracts key information such as plot number, operation type, operation time, pesticide name and dosage, and equipment ID, and performs hash calculations to generate summary data.

[0094] Step 3: Submit a cross-chain request: Smart contract 2 submits the generated regulatory summary along with the Merkel proof of the original transaction to the request queue of cross-chain relay C4 to prove that the transaction does exist in a block of the production subchain.

[0095] Step 4: Cross-chain relay verification: The cross-chain relay's validator set 310 verifies the submitted Merkel proof to confirm the existence and validity of the transaction on the production subchain. Upon successful verification, the relay chain generates a cross-chain event containing a regulatory summary and verification proof.

[0096] Step 5: Regulatory main chain reception: The node of the regulatory main chain C5 listens to the new event generated by the cross-chain relay, packages the regulatory summary and verification proof into the new block of the regulatory main chain, and generates the regulatory evidence hash REG001.

[0097] Step 6: Query and Verification: When regulators need to verify a batch of tobacco leaves, they can query the corresponding regulatory summary through the main regulatory chain and confirm the existence and immutability of the batch data through the verification proof provided by the cross-chain relay without accessing the detailed data of the production sub-chain.

[0098] In Figure 3, solid lines with arrows represent data flow, while dashed lines with arrows represent verification requests and responses. This mechanism achieves a "data available but not visible" regulatory model, protecting enterprise data privacy while meeting the government's need for penetrating oversight.

[0099] Figure 4 This is a sequence diagram of the query response from consumer scanning the code to on-chain data verification in this invention, demonstrating the complete interactive process by which consumers or regulators obtain traceability information through a trusted query service platform.

[0100] like Figure 4 As shown, the query response sequence involves five main roles: Consumer / Regulator Terminal 401, Trusted Query Service Platform D1, Production Alliance Sub-chain Node 402, Regulatory Main Chain Node 403, and Data Anchoring Terminal B1. In the diagram, the vertical axis represents time progression from top to bottom, the horizontal axis represents the various roles involved in the interaction from left to right, solid lines with arrows represent request messages, and dashed lines with arrows represent response messages.

[0101] The query process is divided into two modes: a lightweight query for consumers and a deep verification for regulators. Figure 4 The complete process is illustrated using in-depth verification by regulators as an example: Step ① Scan / Enter Batch Number: The regulator scans the QR code on the cigarette pack using terminal 401 or manually enters the batch number, such as “BATCH2025082001”, to initiate a query request.

[0102] Step 2: Request forwarding to the query platform: Terminal 401 sends the query request to the trusted query service platform D1. The request includes the query type deep verification, batch number, and the queryer's digital certificate for authorization verification.

[0103] Step 3: The platform parses the batch number and locates the on-chain index: The trusted query service platform D1 parses the batch number, identifies the production year, production area, processing plant and other information corresponding to the batch according to the batch coding rules, queries the local cache or metadata index, and obtains the regulatory evidence hash REG001 on the regulatory main chain and the starting transaction hash TX001 on the production alliance sub-chain for the batch.

[0104] Step 4: Request verification from the regulatory main chain: Platform D1 sends a verification request to the regulatory main chain node 403. The request includes the regulatory evidence hash REG001 and requests the corresponding regulatory summary data and cross-chain verification proof.

[0105] Step 5: The main chain of the regulatory body returns a digest and proof: The main chain node 403 retrieves the corresponding block data from the chain based on the hash value and returns a regulatory digest containing a list of hashes of key agricultural events, as well as the Merkel proof and cross-chain relay verification signature corresponding to the digest.

[0106] Step 6: Request detailed verification to the production subchain: Based on the event hash list in the regulatory summary, platform D1 sends a detailed verification request to node 402 of the production alliance subchain, requesting verification of the existence and integrity of specific transaction hashes such as TX001, TX100, etc.

[0107] Step 7: Production subchain returns Merkel proof: Production consortium subchain node 402 retrieves the corresponding Merkel proof from the locally stored block based on the requested transaction hash and returns it to the platform. The Merkel proof can prove that a transaction does indeed exist in a specific block without revealing other transaction information.

[0108] Step 8: Platform Generates Verification Report: Platform D1 integrates the summary information obtained from the regulatory main chain and the Merkel proof obtained from the production sub-chain to generate a traceability report containing multi-level verification results. The report includes: basic batch information, origin, variety, harvest time, a list of key agricultural events, the time, location, and operation type of each event, the on-chain transaction hash corresponding to each event, and Merkel proof data that can be used for independent verification.

[0109] Step 9: Returning Query Results: Platform D1 returns the generated traceability report to the regulator's terminal 401. For regulators with technical capabilities, the transaction hashes and Merkel proofs in the report can be input into a blockchain explorer to independently verify whether the data blocks corresponding to these hashes exist, whether the content is consistent with the report, and whether they have never been tampered with since being uploaded to the blockchain.

[0110] Step 10: Optional verification with the anchor terminal: For the highest level of penetration verification, the regulator can request platform D1 or directly contact the data anchor terminal B1 to verify whether the BeiDou spatiotemporal stamp and device digital fingerprint in the original data packet are consistent with the on-chain record. Anchor terminal B1 can return a locally stored digital digest, which can be compared with the on-chain record to confirm that the data source has not been tampered with.

[0111] Figure 4 Each message is marked with a sequential number and a brief description. This complete query and verification process ensures that any user can trace the entire lifecycle of tobacco leaves from the field to the finished product in a cryptographically verifiable manner.

[0112] This application also has the following beneficial effects: 1. This application overcomes the shortcomings of existing centralized traceability systems, such as easy data tampering, high trust costs, and fragile traceability chains, by providing a trusted traceability system that deeply integrates IoT, BeiDou navigation, and blockchain technologies. This system aims to establish an immutable "digital ID card" for each tobacco leaf throughout its entire lifecycle, without relying on any single centralized institution for endorsement, thereby enhancing industry transparency, strengthening quality supervision, and empowering brand value.

[0113] 2. Trusted Anchoring of Data Sources Based on BeiDou and Hardware Fingerprints: This is the cornerstone of ensuring that "on-chain data is real-world event." The anchoring terminal integrated into the intelligent equipment in this application is not a simple data collector. Each time a critical operation is recorded (e.g., "when and where to perform bud suppression on plot A01"), the BeiDou satellite navigation system is forcibly invoked to obtain the current millisecond-level time and centimeter / meter-level positioning coordinates, generating a spatiotemporal stamp. Simultaneously, the unique cryptographic fingerprint of the security chip embedded in the terminal is read. The business data, spatiotemporal stamp, and device fingerprint are hashed together and then uploaded to the blockchain. Any tampering with the data will cause a drastic change in the hash value, and the unforgeability of the spatiotemporal stamp and device fingerprint technically eliminates the possibility of fabricating operation records afterward. This is fundamentally different from existing technologies that rely solely on sensor data uploads or only encrypt the positioning information itself.

[0114] 3. A Multi-Chain Collaborative Model Adapted to Industry Governance Structures: A single blockchain cannot meet diverse needs. This application adopts an architecture that separates yet connects the production sub-chain and the regulatory main chain. The production sub-chain (consortium chain) is jointly maintained by entities within the industry, storing all detailed data, ensuring data sovereignty and commercial privacy, and achieving high consensus efficiency. The regulatory main chain has higher authority, synchronizing only key, non-repudiable regulatory summaries (such as "a batch of tobacco leaves was found to have excessive pesticide residues, and the corresponding harvesting record hash is XXX") through cross-chain technology. This satisfies the government's need for penetrating supervision while avoiding excessive intervention by regulatory nodes in the details of enterprise operations, aligning with real-world governance logic. Existing blockchain traceability solutions mostly adopt a single-chain structure, making it difficult to balance privacy and regulatory requirements.

[0115] 4. Smart Contract-Driven Dynamic Traceability Logic Generation: Traditional traceability relies on predefined static association tables. This invention utilizes the automation and programmability of smart contracts to achieve dynamic generation of traceability logic. The data association contract acts like a "digital glue," continuously monitoring on-chain events. When it identifies events such as seedling cultivation, transplanting, and fertilization belonging to the same geographical unit (defined by BeiDou coordinates), as well as harvesting events with the same "batch ID," it automatically executes code to "stitch" these discrete events into a dynamic batch file with causal and temporal relationships. This traceability logic is generated automatically in real-time and recorded on the blockchain, preventing human intervention and ensuring the rigor and automation of the traceability chain.

[0116] 5. Novel and innovative technology integration: This invention deeply integrates BeiDou spatiotemporal services, hardware security modules, and a multi-chain blockchain architecture, applying them to the entire agricultural product traceability chain. It constructs an integrated "air-space-ground-network" trusted data collection and storage system, employing a unique technological approach. Compared to existing technologies that only encrypt BeiDou positioning information or only use blockchain storage, this invention achieves end-to-end trust from the data source to the terminal query.

[0117] 6. Significantly Enhanced Practical Value and Trust: It fundamentally solves the "trust" problem of traceability data. It provides consumers with "genuine traceability" that can be verified with a simple scan, and provides technical endorsement for building high-quality, reasonably priced brands; it provides regulators with an efficient and low-cost penetrating regulatory tool; and it provides producers with new means of data assetization and refined management.

[0118] To further explain this invention in detail, the traceability of a package of finished cigarette sheets will be used as an example to illustrate the invention. The following embodiments are for illustrative purposes only and do not constitute a limitation on the scope of protection of this invention.

[0119] Example: Full-chain traceability verification of a package of "K326 Middle Leaf" finished product 1. Data source anchoring and on-chain (production stage): Environmental Monitoring: Environmental data is continuously collected at plot A01 via sensing nodes. Each built-in anchored terminal generates a data packet per hour, containing: "Temperature 28.5℃, Humidity 65%, Soil Moisture 22%", along with a BeiDou geospatial stamp: "Time: 2025-07-08 10:00:00.123; Coordinates: N39°12'34.5678", E116°45'12.3456" and the device fingerprint "DEV_ID_002". This data packet is uploaded to the blockchain after consensus, with a transaction hash of TX000.

[0120] Bud suppression operation: The bud suppression machine operates on plot A01. Its built-in anchoring terminal automatically records: "Operation type: chemical bud suppression; Agent: terbufotoxin; Dilution ratio: 1:50". Simultaneously, it obtains the BeiDou geospatial stamp: "Time: 2025-07-08 10:30:25.123; Coordinates: N39°12'34.5678\",E116°45'12.3456\" and the device fingerprint "DEV_ID_003". After hashing these three data points, the data packet is sent to the production alliance sub-chain node, and after consensus, it is uploaded to the blockchain with a transaction hash of TX001.

[0121] Plant protection operation: The plant protection machine carried out preventative spraying of pesticides on plot A01. The anchor terminal record reads: "Operation type: Plant protection; Pesticide: Bordeaux mixture; Dosage: 0.5L / acre". The record is then uploaded to the blockchain with a BeiDou time stamp and device fingerprint, and the transaction hash is TX050.

[0122] Harvesting operation: The harvester harvests the leaves in the middle section of plot A01. Anchor terminal record: "Batch number: BATCH2025082001; Leaf position: 6-10; Average CARI value: 8.5". The BeiDou time stamp and device fingerprint are then uploaded to the blockchain, with the transaction hash TX100.

[0123] 2. Dynamic aggregation of smart contracts: The data-associating smart contract C2 on the production alliance subchain continuously monitors all on-chain events. When it receives TX100 (harvesting operation), the contract parses the batch number "BATCH2025082001" and the plot coordinate range.

[0124] By reviewing the contracts over the past six months, all events originating from the same plot of land (around N39°12') were found to be related to this plot of land, including TX000 (environment), TX001 (bud suppression), and TX050 (plant protection).

[0125] The contract automatically executes code to logically associate TX000, TX001, TX050, and TX100 according to their timestamp order, forming a dynamic "BATCH2025082001 batch file", and records this association on the chain, generating a new associated transaction hash TX101.

[0126] 3. Processing and cross-chain supervision are synchronized: The harvested tobacco leaves enter the curing plant, and the curing process data for each curing batch is also uploaded to the blockchain in an anchored manner. After curing is completed, a curing batch number "CURE2025082501" is generated and associated with the harvesting batch "BATCH2025082001".

[0127] After being cured, the tobacco leaves are sent to the re-drying plant for further processing and drying, forming the final finished batch "FINAL_BATCH_X". The re-drying plant system generates batch association information and declares on the blockchain: "FINAL_BATCH_X originates from [CURE2025082501, ...]", which is signed by the re-drying plant node, and the transaction hash is TX200.

[0128] Smart Contract 3 (Regulatory Summary Generation Contract) detects the generation of the final product batch and automatically extracts key regulatory information: product batch number, raw material origin (near N39°12'), key agricultural record hashes (TX001, TX050, etc.), baking record hashes, etc., and generates a regulatory summary data package.

[0129] The regulatory summary data packet is synchronized to the regulatory main chain C5 via cross-chain relay C4, generating a regulatory evidence hash REG001. Regulatory agencies can query key information for this batch through the main chain, but cannot directly access the full detailed data on the production sub-chain.

[0130] 4. Consumer inquiry and verification: A consumer buys a pack of cigarettes and scans the QR code on the packaging. The QR code contains the key index "FINAL_BATCH_X".

[0131] The query request was sent to the trusted query service platform D1. The platform first queried the index "FINAL_BATCH_X" on the regulatory main chain to obtain the regulatory summary corresponding to the regulatory evidence hash REG001, from which it was learned that the finished product batch originated from the harvest batch "BATCH2025082001".

[0132] The platform then uses the harvest batch number to query the associated transaction TX101 on the production subchain and obtains all related events such as TX000, TX001, TX050, and TX100.

[0133] The platform generates a visual traceability report, displayed on the consumer's mobile device: "The raw materials for this product were harvested on xx / xx / xx from plot A01 near N39°12'. During the growth period, bud suppression was carried out on July 8th (using the pesticide: methoxyfenozide), and preventative disease spraying was carried out on July 15th (using the pesticide: Bordeaux mixture). The baking process is a three-stage adaptive baking..." The report also includes the verification code and key transaction hash for this query at the bottom.

[0134] Consumers with technical skills can input the transaction hashes (such as TX001) in the report into a blockchain explorer to independently verify whether the data blocks corresponding to these hashes exist, whether the content is consistent with the report, and whether they have never been tampered with since being uploaded to the blockchain (the hash value has not changed).

[0135] 5. Regulatory penetration inspection: Suppose that a regulatory agency finds that the pesticide residue in batch "FINAL_BATCH_X" exceeds the standard during a random inspection. The regulator enters the batch number "FINAL_BATCH_X" through the deep verification channel D3.

[0136] The system returns the regulatory summary corresponding to the regulatory evidence hash REG001, which contains the transaction hash TX050 of the plant protection operation.

[0137] The regulator requests a thorough verification of TX050 through the cross-chain verification interface provided by the main regulatory chain. The main regulatory chain initiates a verification request to the production sub-chain via the cross-chain relay C4 to obtain the Merkel proof of the block containing TX050.

[0138] Regulators can verify that the TX050 record clearly states the pesticide type as "Bordeaux mixture" and the dosage as "0.5L / acre," and that this record was generated by device "DEV_ID_004" at a specific time (2025-07-15 09:20:00) and location (N39°12'34.5678"). If the investigation finds that the device was not operating in the area at the time, or that the record has been tampered with, the immutability of the blockchain will provide irrefutable evidence, quickly pinpointing the responsible party.

[0139] Summary of Implementation Results: This system successfully established a complete and reliable digital archive for each pack of finished cigarettes, from farm to shelf. Consumers can obtain reliable information on the origin and agricultural activities by scanning the code, regulators can achieve transparent supervision without infringing on corporate privacy, and producers can use blockchain technology to provide an immutable technical endorsement for the quality of their products. During system operation, all data is anchored to BeiDou time and space stamps and device fingerprints at the source, ensuring that "on-chain data is real-world events."

[0140] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A reliable traceability system for the entire life cycle quality of tobacco leaves, characterized in that, include: Multiple IoT data anchoring terminals are deployed at the operating equipment or key nodes in various stages of tobacco production. Each IoT data anchoring terminal is used to collect business data when the corresponding key event is triggered, and generate a source data packet with a trusted spatiotemporal stamp and a unique device identity certificate. A multi-chain collaborative blockchain network is connected to the IoT data anchoring terminal and consists of at least one production alliance sub-chain and at least one regulatory main chain. The production alliance sub-chain is used to perform consensus verification on the source data packet and then distribute the evidence. The regulatory main chain is used to receive and store the aggregated key regulatory summary data from the production alliance sub-chain through a cross-chain protocol. The trusted query service platform communicates with the multi-chain collaborative blockchain network and is also connected to the production alliance sub-chain and the regulatory main chain. It is used to receive query requests and, according to the permission level of the query request, retrieve and return traceability information or verification proof based on the blockchain-stored evidence data.

2. The system according to claim 1, characterized in that, The IoT data anchoring terminal integrates a Beidou / GNSS positioning module, a security chip storing a unique device digital fingerprint, and a communication module. The generation of the source data packet with a trusted spatiotemporal stamp and a unique device identity credential includes: In response to a trigger signal generated when the operating equipment performs a critical operation, the corresponding service data is received; The BeiDou / GNSS positioning module is invoked to obtain the current UTC time and geographic coordinates, and a BeiDou time-space stamp is generated. Read the unique device digital fingerprint from the security chip; At least the business data, the BeiDou time stamp, and the device digital fingerprint are hashed to generate a digital digest; The business data, BeiDou time stamp, device digital fingerprint, and digital digest are encapsulated into the source data packet and broadcast to the multi-chain collaborative blockchain network through the communication module.

3. The system according to claim 1, characterized in that, The operating mechanism of the multi-chain collaborative blockchain network includes: The production alliance subchain consists of multiple entity nodes in the tobacco industry chain and adopts a practical Byzantine fault-tolerant consensus algorithm to store data in the production process. Data association smart contracts and regulatory summary generation smart contracts are deployed on the production alliance subchain. The regulatory main chain consists of at least one industry regulatory agency node, which is used to store key regulatory summary data and violation evidence hash values ​​generated by the regulatory summary generation smart contract and synchronized through the cross-chain protocol.

4. The system according to claim 3, characterized in that, The multi-chain collaborative blockchain network also includes a cross-chain relay component; The cross-chain protocol is configured as follows: the cross-chain relay component receives a cross-chain request submitted by the regulatory summary generation smart contract, which contains the key regulatory summary data and the original transaction Merkel proof; after verifying the validity of the Merkel proof, the key regulatory summary data is packaged into a new block of the regulatory main chain, and a regulatory evidence hash is generated. The main regulatory chain can verify the authenticity of the evidence data stored on the production alliance sub-chain based on the regulatory evidence hash and the Merkel proof, but it does not have the right to directly access the full amount of detailed data stored on the production alliance sub-chain.

5. The system according to claim 3, characterized in that, The data-associated smart contract is configured to execute the following logic: Continuously monitor and receive multiple source data packets that are stored on the production alliance subchain and carry the same geographic unit identifier or the same batch code; According to the time order of the BeiDou time and space stamps in each source data packet, the source data packets representing discrete agricultural events are automatically linked together into an event chain with a temporal logical relationship. In response to receiving a source data packet from the harvesting stage that carries a specific batch code, the system automatically digitally binds the batch code to all concatenated source data packets belonging to the same production unit, generating and storing a complete digital batch file.

6. The system according to claim 5, characterized in that, Before the data-associated smart contract executes the listening and receiving logic, it also includes: Parse the geographic coordinates contained in the BeiDou time and space stamp in each source data packet, determine whether the geographic coordinates fall within one or more predefined geofences, and associate the source data packets that are determined to fall within the same geofence with the same geographic unit identifier.

7. The system according to claim 6, characterized in that, In the source data packet generated by the IoT data anchoring terminal, the BeiDou spatiotemporal stamp and the device digital fingerprint together constitute two-factor anchoring information, which is used to provide dual proof of spatiotemporal non-repudiation and device identity non-forgeability for the business data. The device digital fingerprint is generated by combining a session random number generated by the true random number generator of the security chip with the device ID to ensure that each generated fingerprint is unique.

8. A method for reliable traceability of tobacco leaf quality throughout its entire lifecycle using the system described in any one of claims 1-7, characterized in that, Includes the following steps: Data source anchoring: When key events occur in each tobacco production process, IoT data anchoring terminals collect business data in real time and attach BeiDou spatiotemporal stamps and device digital fingerprints to the business data to generate trusted source data packets. Subchain consensus notarization involves broadcasting the trusted source data packet to the production alliance subchain network. After consensus verification by multiple nodes, the packet is packaged and uploaded to the chain to form an immutable notarization record. Smart contract aggregation is achieved by data-associating smart contracts deployed on the production alliance subchain, which continuously and dynamically aggregate the evidence records corresponding to multiple discrete events belonging to the same production unit or the same batch into logically associated batch files. Cross-chain regulatory synchronization involves a regulatory summary generation smart contract extracting key regulatory summary information from the batch of files and synchronizing this key regulatory summary information to the regulatory main chain for evidence storage via a cross-chain protocol. Multi-role trusted query: The trusted query service platform receives and responds to query requests from different roles, and returns traceability information or encrypted proof of data integrity backed by blockchain according to the query permissions.

9. The method according to claim 8, characterized in that, The batch files that are dynamically aggregated into logical relationships include: The data association smart contract parses the BeiDou time and space stamp in each trusted source data packet and extracts its geographical coordinates; Call the pre-configured geofence database to determine the parcel identifier to which the geographic coordinates belong; Trusted source data packets with the same plot identifier but different operation types are sorted and linked according to the time sequence of their BeiDou spatiotemporal stamps to form a time-series agricultural operation chain for that plot. When a trusted source data packet carrying a batch code is received during the harvesting process, the entire time-series agricultural operation chain is hashed and associated with the batch code to generate a digital batch file.

10. The method according to claim 8, characterized in that, The method of returning blockchain-backed cryptographic proof of traceability information or data integrity based on query permissions includes: In response to a lightweight query request from a consumer terminal, the trusted query service platform retrieves and returns a key information graph corresponding to the query identifier from the regulatory main chain or the production alliance sub-chain. The key information graph includes at least a visual summary of the place of origin, harvest time, and major agricultural records. In response to a deep verification request from the regulator's terminal, the trusted query service platform calls the cross-chain verification interface to obtain the regulatory evidence hash and cross-chain verification proof corresponding to the target batch from the main regulatory chain, and to obtain the transaction hash and Merkel proof corresponding to the specific agricultural event from the production alliance sub-chain. The platform then generates and returns a comprehensive verification report containing multi-layered encrypted verification results, which can be verified by an independent blockchain explorer.

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